A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

Algorithm 3

HBD.

Initiate the population and the historical population randomly sampled from search space.

While (Stop Condition doesn’t meet)

 Perform the first type selection: in the case of , where   and are drawn from uniformly distribution with the

 range between 0 and 1.

 Permute arbitrary changes in position of .

 Generate the mutant according to (1).

 Generate the population based on Algorithm 1.

 Perform the second type selection: select the population with better fitness from and .

 Update the best solution.

 //Invoke DE with exploitive strategy

 Select One Individual according to its probability: .

 Optimize with the help of DE, and get

 If (fitness( <= fitness())

 End If

 Update the best solution.

End While

Output the best solution.